Feature extraction of search product based on multi-feature fusion-oriented to Chinese online reviews

Data Science and Management Pub Date : 2022-06-01 DOI:10.1016/j.dsm.2022.04.002

Xunjiang Huang, Yaqian Liu, Yang Wang, Xue Wang

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引用次数: 3

Abstract

The increasing Chinese online reviews contain rich product demand information, especially for search products. This study suggests a product feature extraction model from online reviews based on multi-feature fusion named PFEMF (products features extraction based on multi-feature fusion) model. Combining sentence and word characteristics of Chinese online reviews, the model explores the lexical features, frequency features, span features, and semantic similarity features of words. And then, they are fused to identify the features that customers are concerned about most by sequential relationship analysis. The identified product feature provides direction for product innovation and facilitates the product selection for customers. Finally, the study takes iPad Air as an example to prove this model. The results show that the extraction performance of the PFEMF model is superior to the traditional term frequency-inverse document frequency (tf-idf) algorithm, word span algorithm, and semantic similarity algorithm.

查看原文本刊更多论文

面向中文在线评论的基于多特征融合的搜索产品特征提取

越来越多的中文在线评论包含了丰富的产品需求信息，尤其是搜索产品。本文提出了一种基于多特征融合的在线评论产品特征提取模型，命名为PFEMF (products features extraction based on multi-feature fusion)模型。该模型结合中文网络评论的句子特征和单词特征，探索单词的词汇特征、频率特征、跨度特征和语义相似特征。然后，通过序列关系分析，将这些特征进行融合，识别出顾客最关心的特征。识别出的产品特征为产品创新提供了方向，为顾客选择产品提供了方便。最后以iPad Air为例对该模型进行验证。结果表明，PFEMF模型的提取性能优于传统的词频逆文档频率(tf-idf)算法、词跨算法和语义相似度算法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Data Science and Management

CiteScore

7.50

自引率

0.00%

发文量